A new clustering algorithm of large datasets with O(N) computational complexity

In fields such as bioinformatics, cytometry, geographic information systems, just to name a few, huge amount of data, often multidimensional in nature, has more than ever highlighted the need for new algorithms to reduce the computational requirements needed for data analysis and interpretation. In this study, we present a new unsupervised clustering algorithm /sub e/nsity-based adaptive window clustering algorithm, which reduces the computational load to /spl sim/ O(N) number of computations, making it more attractive and faster than current hierarchical algorithms. This method relies on weighting a dataset to grid points on a mesh, and identifies the density peaks by reducing low density points, ranking and correlation calculation. The adaptive windows used are a modification of the recently proposed k-windows clustering algorithm to shape the desired clusters. The new algorithm makes it easier for users to observe and analyze data for enhanced interpretation and improved real-world applications, especially in clinical practices.